Update app.py

app.py

@@ -9,6 +9,7 @@ from collections import Counter
 import numpy as np
 from transformers import pipeline
 classifier = pipeline('sentiment-analysis')
+summarizer= pipeline("summarization", max_length=10)
 #hashtag_phrase ="#datascience"
 #recent_tweet_count_you_want =100
 def search_hashtag1(hashtag_phrase,recent_tweet_count_you_want):
@@ -48,14 +49,18 @@ def search_hashtag1(hashtag_phrase,recent_tweet_count_you_want):
   q=[p[i]['label'] for i in range(len(p))]
   data10=pd.DataFrame(q,columns={"sentiment"})
   data_tweet_final=pd.concat([data6,data10],axis=1)
-  data_tweet_final.to_csv("tweet_data2.csv")
+  p_summarize_label = [i for i in summarizer(tweet_list)]
+  q_summarize=[p_summarize_label[i]['summary_text'] for i in range(len(p_summarize_label))]
+  data_summarize=pd.DataFrame(q_summarize,columns={"summarized_tweets"})
+  data_tweet_summarize_final=pd.concat([data_tweet_final,data_summarize],axis=1)
+  data_tweet_summarize_final.to_csv("tweet_data2.csv")
   #data6.to_csv("tweet_data1.csv")
   #data6=data5.head(10)
-  return data_tweet_final
+  return data_tweet_summarize_final
 iface = gr.Interface(
     search_hashtag1,inputs=["text","number"],
     outputs="dataframe",
-    examples=[["#datascience",10],["#valentine's day",100],["#pushpa",200],["#budget",500],["#sharktankindia",300]],
+    examples=[["#datascience",5],["#valentine's day",10],["#pushpa",15],["#budget",20],["#sharktankindia",30]],
     theme="seafoam",
     title='Sakil Tweetlib6 App',
     description="You can extract tweets based on Hashtag.e.g. Please enter #datascience. The app extracts tweets based on the hashtag and the number of tweet count you want.")